/* ---------------------------------------------------------------------
 *
 * Copyright (C) 2019 - 2023 by the deal.II authors
 *
 * This file is part of the deal.II library.
 *
 * The deal.II library is free software; you can use it, redistribute
 * it, and/or modify it under the terms of the GNU Lesser General
 * Public License as published by the Free Software Foundation; either
 * version 2.1 of the License, or (at your option) any later version.
 * The full text of the license can be found in the file LICENSE.md at
 * the top level directory of deal.II.
 *
 * ---------------------------------------------------------------------

 *
 * Author: Thomas C. Clevenger, Clemson University
 *         Timo Heister, Clemson University
 *         Guido Kanschat, Heidelberg University
 *         Martin Kronbichler, Technical University of Munich
 */


// @sect3{Include files}

// The include files are a combination of step-40, step-16, and step-37:

#include <deal.II/base/conditional_ostream.h>
#include <deal.II/base/data_out_base.h>
#include <deal.II/base/index_set.h>
#include <deal.II/base/logstream.h>
#include <deal.II/base/quadrature_lib.h>
#include <deal.II/base/timer.h>
#include <deal.II/base/parameter_handler.h>
#include <deal.II/distributed/grid_refinement.h>
#include <deal.II/distributed/tria.h>
#include <deal.II/dofs/dof_tools.h>
#include <deal.II/fe/fe_q.h>
#include <deal.II/fe/fe_values.h>
#include <deal.II/fe/mapping_q1.h>
#include <deal.II/grid/grid_generator.h>
#include <deal.II/grid/grid_refinement.h>
#include <deal.II/grid/tria.h>
#include <deal.II/lac/affine_constraints.h>
#include <deal.II/lac/dynamic_sparsity_pattern.h>
#include <deal.II/lac/sparsity_tools.h>
#include <deal.II/lac/solver_cg.h>

// We use the same strategy as in step-40 to switch between PETSc and
// Trilinos:

#include <deal.II/lac/generic_linear_algebra.h>

// Comment the following preprocessor definition in or out if you have
// PETSc and Trilinos installed and you prefer using PETSc in this
// example:
#define FORCE_USE_OF_TRILINOS

namespace LA
{
#if defined(DEAL_II_WITH_PETSC) && !defined(DEAL_II_PETSC_WITH_COMPLEX) && \
  !(defined(DEAL_II_WITH_TRILINOS) && defined(FORCE_USE_OF_TRILINOS))
  using namespace dealii::LinearAlgebraPETSc;
#  define USE_PETSC_LA
#elif defined(DEAL_II_WITH_TRILINOS)
  using namespace dealii::LinearAlgebraTrilinos;
#else
#  error DEAL_II_WITH_PETSC or DEAL_II_WITH_TRILINOS required
#endif
} // namespace LA

#include <deal.II/matrix_free/matrix_free.h>
#include <deal.II/matrix_free/operators.h>
#include <deal.II/matrix_free/fe_evaluation.h>
#include <deal.II/multigrid/mg_coarse.h>
#include <deal.II/multigrid/mg_constrained_dofs.h>
#include <deal.II/multigrid/mg_matrix.h>
#include <deal.II/multigrid/mg_smoother.h>
#include <deal.II/multigrid/mg_tools.h>
#include <deal.II/multigrid/mg_transfer.h>
#include <deal.II/multigrid/multigrid.h>
#include <deal.II/multigrid/mg_transfer_matrix_free.h>
#include <deal.II/numerics/data_out.h>
#include <deal.II/numerics/vector_tools.h>

// The following files are used to assemble the error estimator like in step-12:
#include <deal.II/fe/fe_interface_values.h>
#include <deal.II/meshworker/mesh_loop.h>

using namespace dealii;


// @sect3{Coefficients and helper classes}

// MatrixFree operators must use the
// LinearAlgebra::distributed::Vector vector type. Here we define
// operations which copy to and from Trilinos vectors for compatibility with
// the matrix-based code. Note that this functionality does not currently
// exist for PETSc vector types, so Trilinos must be installed to use the
// MatrixFree solver in this tutorial.
namespace ChangeVectorTypes
{
  template <typename number>
  void copy(LA::MPI::Vector &                                 out,
            const LinearAlgebra::distributed::Vector<number> &in)
  {
    LinearAlgebra::ReadWriteVector<double> rwv(out.locally_owned_elements());
    rwv.import_elements(in, VectorOperation::insert);
#ifdef USE_PETSC_LA
    AssertThrow(false,
                ExcMessage("CopyVectorTypes::copy() not implemented for "
                           "PETSc vector types."));
#else
    out.import_elements(rwv, VectorOperation::insert);
#endif
  }



  template <typename number>
  void copy(LinearAlgebra::distributed::Vector<number> &out,
            const LA::MPI::Vector &                     in)
  {
    LinearAlgebra::ReadWriteVector<double> rwv;
#ifdef USE_PETSC_LA
    (void)in;
    AssertThrow(false,
                ExcMessage("CopyVectorTypes::copy() not implemented for "
                           "PETSc vector types."));
#else
    rwv.reinit(in);
#endif
    out.import_elements(rwv, VectorOperation::insert);
  }
} // namespace ChangeVectorTypes


// Let's move on to the description of the problem we want to solve.
// We set the right-hand side function to 1.0. The @p value function returning a
// VectorizedArray is used by the matrix-free code path.
template <int dim>
class RightHandSide : public Function<dim>
{
public:
  virtual double value(const Point<dim> & /*p*/,
                       const unsigned int /*component*/ = 0) const override
  {
    return 1.0;
  }


  template <typename number>
  VectorizedArray<number>
  value(const Point<dim, VectorizedArray<number>> & /*p*/,
        const unsigned int /*component*/ = 0) const
  {
    return VectorizedArray<number>(1.0);
  }
};


// This next class represents the diffusion coefficient. We use a variable
// coefficient which is 100.0 at any point where at least one coordinate is
// less than -0.5, and 1.0 at all other points. As above, a separate value()
// returning a VectorizedArray is used for the matrix-free code. An @p
// average() function computes the arithmetic average for a set of points.
template <int dim>
class Coefficient : public Function<dim>
{
public:
  virtual double value(const Point<dim> &p,
                       const unsigned int /*component*/ = 0) const override;

  template <typename number>
  VectorizedArray<number> value(const Point<dim, VectorizedArray<number>> &p,
                                const unsigned int /*component*/ = 0) const;

  template <typename number>
  number average_value(const std::vector<Point<dim, number>> &points) const;

  // When using a coefficient in the MatrixFree framework, we also
  // need a function that creates a Table of coefficient values for a
  // set of cells provided by the MatrixFree operator argument here.
  template <typename number>
  std::shared_ptr<Table<2, VectorizedArray<number>>> make_coefficient_table(
    const MatrixFree<dim, number, VectorizedArray<number>> &mf_storage) const;
};



template <int dim>
double Coefficient<dim>::value(const Point<dim> &p, const unsigned int) const
{
  for (int d = 0; d < dim; ++d)
    {
      if (p[d] < -0.5)
        return 100.0;
    }
  return 1.0;
}



template <int dim>
template <typename number>
VectorizedArray<number>
Coefficient<dim>::value(const Point<dim, VectorizedArray<number>> &p,
                        const unsigned int) const
{
  VectorizedArray<number> return_value = VectorizedArray<number>(1.0);
  for (unsigned int i = 0; i < VectorizedArray<number>::size(); ++i)
    {
      for (int d = 0; d < dim; ++d)
        if (p[d][i] < -0.5)
          {
            return_value[i] = 100.0;
            break;
          }
    }

  return return_value;
}



template <int dim>
template <typename number>
number Coefficient<dim>::average_value(
  const std::vector<Point<dim, number>> &points) const
{
  number average(0);
  for (unsigned int i = 0; i < points.size(); ++i)
    average += value(points[i]);
  average /= points.size();

  return average;
}



template <int dim>
template <typename number>
std::shared_ptr<Table<2, VectorizedArray<number>>>
Coefficient<dim>::make_coefficient_table(
  const MatrixFree<dim, number, VectorizedArray<number>> &mf_storage) const
{
  auto coefficient_table =
    std::make_shared<Table<2, VectorizedArray<number>>>();

  FEEvaluation<dim, -1, 0, 1, number> fe_eval(mf_storage);

  const unsigned int n_cells    = mf_storage.n_cell_batches();
  const unsigned int n_q_points = fe_eval.n_q_points;

  coefficient_table->reinit(n_cells, 1);

  for (unsigned int cell = 0; cell < n_cells; ++cell)
    {
      fe_eval.reinit(cell);

      VectorizedArray<number> average_value = 0.;
      for (unsigned int q = 0; q < n_q_points; ++q)
        average_value += value(fe_eval.quadrature_point(q));
      average_value /= n_q_points;

      (*coefficient_table)(cell, 0) = average_value;
    }

  return coefficient_table;
}



// @sect3{Run time parameters}

// We will use ParameterHandler to pass in parameters at runtime.  The
// structure @p Settings parses and stores these parameters to be queried
// throughout the program.
struct Settings
{
  bool try_parse(const std::string &prm_filename);

  enum SolverType
  {
    gmg_mb,
    gmg_mf,
    amg
  };

  SolverType solver;

  int          dimension;
  double       smoother_dampen;
  unsigned int smoother_steps;
  unsigned int n_steps;
  bool         output;
};



bool Settings::try_parse(const std::string &prm_filename)
{
  ParameterHandler prm;
  prm.declare_entry("dim", "2", Patterns::Integer(), "The problem dimension.");
  prm.declare_entry("n_steps",
                    "10",
                    Patterns::Integer(0),
                    "Number of adaptive refinement steps.");
  prm.declare_entry("smoother dampen",
                    "1.0",
                    Patterns::Double(0.0),
                    "Dampen factor for the smoother.");
  prm.declare_entry("smoother steps",
                    "1",
                    Patterns::Integer(1),
                    "Number of smoother steps.");
  prm.declare_entry("solver",
                    "MF",
                    Patterns::Selection("MF|MB|AMG"),
                    "Switch between matrix-free GMG, "
                    "matrix-based GMG, and AMG.");
  prm.declare_entry("output",
                    "false",
                    Patterns::Bool(),
                    "Output graphical results.");

  if (prm_filename.size() == 0)
    {
      std::cout << "****  Error: No input file provided!\n"
                << "****  Error: Call this program as './step-50 input.prm\n"
                << '\n'
                << "****  You may want to use one of the input files in this\n"
                << "****  directory, or use the following default values\n"
                << "****  to create an input file:\n";
      if (Utilities::MPI::this_mpi_process(MPI_COMM_WORLD) == 0)
        prm.print_parameters(std::cout, ParameterHandler::Text);
      return false;
    }

  try
    {
      prm.parse_input(prm_filename);
    }
  catch (std::exception &e)
    {
      if (Utilities::MPI::this_mpi_process(MPI_COMM_WORLD) == 0)
        std::cerr << e.what() << std::endl;
      return false;
    }

  if (prm.get("solver") == "MF")
    this->solver = gmg_mf;
  else if (prm.get("solver") == "MB")
    this->solver = gmg_mb;
  else if (prm.get("solver") == "AMG")
    this->solver = amg;
  else
    AssertThrow(false, ExcNotImplemented());

  this->dimension       = prm.get_integer("dim");
  this->n_steps         = prm.get_integer("n_steps");
  this->smoother_dampen = prm.get_double("smoother dampen");
  this->smoother_steps  = prm.get_integer("smoother steps");
  this->output          = prm.get_bool("output");

  return true;
}



// @sect3{LaplaceProblem class}

// This is the main class of the program. It looks very similar to
// step-16, step-37, and step-40. For the MatrixFree setup, we use the
// MatrixFreeOperators::LaplaceOperator class which defines `local_apply()`,
// `compute_diagonal()`, and `set_coefficient()` functions internally. Note that
// the polynomial degree is a template parameter of this class. This is
// necessary for the matrix-free code.
template <int dim, int degree>
class LaplaceProblem
{
public:
  LaplaceProblem(const Settings &settings);
  void run();

private:
  // We will use the following types throughout the program. First the
  // matrix-based types, after that the matrix-free classes. For the
  // matrix-free implementation, we use @p float for the level operators.
  using MatrixType      = LA::MPI::SparseMatrix;
  using VectorType      = LA::MPI::Vector;
  using PreconditionAMG = LA::MPI::PreconditionAMG;

  using MatrixFreeLevelMatrix = MatrixFreeOperators::LaplaceOperator<
    dim,
    degree,
    degree + 1,
    1,
    LinearAlgebra::distributed::Vector<float>>;
  using MatrixFreeActiveMatrix = MatrixFreeOperators::LaplaceOperator<
    dim,
    degree,
    degree + 1,
    1,
    LinearAlgebra::distributed::Vector<double>>;

  using MatrixFreeLevelVector  = LinearAlgebra::distributed::Vector<float>;
  using MatrixFreeActiveVector = LinearAlgebra::distributed::Vector<double>;

  void setup_system();
  void setup_multigrid();
  void assemble_system();
  void assemble_multigrid();
  void assemble_rhs();
  void solve();
  void estimate();
  void refine_grid();
  void output_results(const unsigned int cycle);

  Settings settings;

  MPI_Comm           mpi_communicator;
  ConditionalOStream pcout;

  parallel::distributed::Triangulation<dim> triangulation;
  const MappingQ1<dim>                      mapping;
  FE_Q<dim>                                 fe;

  DoFHandler<dim> dof_handler;

  IndexSet                  locally_owned_dofs;
  IndexSet                  locally_relevant_dofs;
  AffineConstraints<double> constraints;

  MatrixType             system_matrix;
  MatrixFreeActiveMatrix mf_system_matrix;
  VectorType             solution;
  VectorType             right_hand_side;
  Vector<double>         estimated_error_square_per_cell;

  MGLevelObject<MatrixType> mg_matrix;
  MGLevelObject<MatrixType> mg_interface_in;
  MGConstrainedDoFs         mg_constrained_dofs;

  MGLevelObject<MatrixFreeLevelMatrix> mf_mg_matrix;

  TimerOutput computing_timer;
};


// The only interesting part about the constructor is that we construct the
// multigrid hierarchy unless we use AMG. For that, we need to parse the
// run time parameters before this constructor completes.
template <int dim, int degree>
LaplaceProblem<dim, degree>::LaplaceProblem(const Settings &settings)
  : settings(settings)
  , mpi_communicator(MPI_COMM_WORLD)
  , pcout(std::cout, (Utilities::MPI::this_mpi_process(mpi_communicator) == 0))
  , triangulation(mpi_communicator,
                  Triangulation<dim>::limit_level_difference_at_vertices,
                  (settings.solver == Settings::amg) ?
                    parallel::distributed::Triangulation<dim>::default_setting :
                    parallel::distributed::Triangulation<
                      dim>::construct_multigrid_hierarchy)
  , mapping()
  , fe(degree)
  , dof_handler(triangulation)
  , computing_timer(pcout, TimerOutput::never, TimerOutput::wall_times)
{
  GridGenerator::hyper_L(triangulation, -1., 1., /*colorize*/ false);
  triangulation.refine_global(1);
}



// @sect4{LaplaceProblem::setup_system()}

// Unlike step-16 and step-37, we split the set up into two parts,
// setup_system() and setup_multigrid(). Here is the typical setup_system()
// function for the active mesh found in most tutorials. For matrix-free, the
// active mesh set up is similar to step-37; for matrix-based (GMG and AMG
// solvers), the setup is similar to step-40.
template <int dim, int degree>
void LaplaceProblem<dim, degree>::setup_system()
{
  TimerOutput::Scope timing(computing_timer, "Setup");

  dof_handler.distribute_dofs(fe);

  locally_relevant_dofs = DoFTools::extract_locally_relevant_dofs(dof_handler);
  locally_owned_dofs    = dof_handler.locally_owned_dofs();

  solution.reinit(locally_owned_dofs, mpi_communicator);
  right_hand_side.reinit(locally_owned_dofs, mpi_communicator);
  constraints.reinit(locally_relevant_dofs);
  DoFTools::make_hanging_node_constraints(dof_handler, constraints);

  VectorTools::interpolate_boundary_values(
    mapping, dof_handler, 0, Functions::ZeroFunction<dim>(), constraints);
  constraints.close();

  switch (settings.solver)
    {
      case Settings::gmg_mf:
        {
          typename MatrixFree<dim, double>::AdditionalData additional_data;
          additional_data.tasks_parallel_scheme =
            MatrixFree<dim, double>::AdditionalData::none;
          additional_data.mapping_update_flags =
            (update_gradients | update_JxW_values | update_quadrature_points);
          std::shared_ptr<MatrixFree<dim, double>> mf_storage =
            std::make_shared<MatrixFree<dim, double>>();
          mf_storage->reinit(mapping,
                             dof_handler,
                             constraints,
                             QGauss<1>(degree + 1),
                             additional_data);

          mf_system_matrix.initialize(mf_storage);

          const Coefficient<dim> coefficient;
          mf_system_matrix.set_coefficient(
            coefficient.make_coefficient_table(*mf_storage));

          break;
        }

      case Settings::gmg_mb:
      case Settings::amg:
        {
#ifdef USE_PETSC_LA
          DynamicSparsityPattern dsp(locally_relevant_dofs);
          DoFTools::make_sparsity_pattern(dof_handler, dsp, constraints);

          SparsityTools::distribute_sparsity_pattern(dsp,
                                                     locally_owned_dofs,
                                                     mpi_communicator,
                                                     locally_relevant_dofs);

          system_matrix.reinit(locally_owned_dofs,
                               locally_owned_dofs,
                               dsp,
                               mpi_communicator);
#else
          TrilinosWrappers::SparsityPattern dsp(locally_owned_dofs,
                                                locally_owned_dofs,
                                                locally_relevant_dofs,
                                                mpi_communicator);
          DoFTools::make_sparsity_pattern(dof_handler, dsp, constraints);
          dsp.compress();
          system_matrix.reinit(dsp);
#endif

          break;
        }

      default:
        Assert(false, ExcNotImplemented());
    }
}

// @sect4{LaplaceProblem::setup_multigrid()}

// This function does the multilevel setup for both matrix-free and
// matrix-based GMG. The matrix-free setup is similar to that of step-37, and
// the matrix-based is similar to step-16, except we must use appropriate
// distributed sparsity patterns.
//
// The function is not called for the AMG approach, but to err on the
// safe side, the main `switch` statement of this function
// nevertheless makes sure that the function only operates on known
// multigrid settings by throwing an assertion if the function were
// called for anything other than the two geometric multigrid methods.
template <int dim, int degree>
void LaplaceProblem<dim, degree>::setup_multigrid()
{
  TimerOutput::Scope timing(computing_timer, "Setup multigrid");

  dof_handler.distribute_mg_dofs();

  mg_constrained_dofs.clear();
  mg_constrained_dofs.initialize(dof_handler);

  const std::set<types::boundary_id> boundary_ids = {types::boundary_id(0)};
  mg_constrained_dofs.make_zero_boundary_constraints(dof_handler, boundary_ids);

  const unsigned int n_levels = triangulation.n_global_levels();

  switch (settings.solver)
    {
      case Settings::gmg_mf:
        {
          mf_mg_matrix.resize(0, n_levels - 1);

          for (unsigned int level = 0; level < n_levels; ++level)
            {
              const IndexSet relevant_dofs =
                DoFTools::extract_locally_relevant_level_dofs(dof_handler,
                                                              level);
              AffineConstraints<double> level_constraints;
              level_constraints.reinit(relevant_dofs);
              level_constraints.add_lines(
                mg_constrained_dofs.get_boundary_indices(level));
              level_constraints.close();

              typename MatrixFree<dim, float>::AdditionalData additional_data;
              additional_data.tasks_parallel_scheme =
                MatrixFree<dim, float>::AdditionalData::none;
              additional_data.mapping_update_flags =
                (update_gradients | update_JxW_values |
                 update_quadrature_points);
              additional_data.mg_level = level;
              std::shared_ptr<MatrixFree<dim, float>> mf_storage_level(
                new MatrixFree<dim, float>());
              mf_storage_level->reinit(mapping,
                                       dof_handler,
                                       level_constraints,
                                       QGauss<1>(degree + 1),
                                       additional_data);

              mf_mg_matrix[level].initialize(mf_storage_level,
                                             mg_constrained_dofs,
                                             level);

              const Coefficient<dim> coefficient;
              mf_mg_matrix[level].set_coefficient(
                coefficient.make_coefficient_table(*mf_storage_level));

              mf_mg_matrix[level].compute_diagonal();
            }

          break;
        }

      case Settings::gmg_mb:
        {
          mg_matrix.resize(0, n_levels - 1);
          mg_matrix.clear_elements();
          mg_interface_in.resize(0, n_levels - 1);
          mg_interface_in.clear_elements();

          for (unsigned int level = 0; level < n_levels; ++level)
            {
              const IndexSet dof_set =
                DoFTools::extract_locally_relevant_level_dofs(dof_handler,
                                                              level);

              {
#ifdef USE_PETSC_LA
                DynamicSparsityPattern dsp(dof_set);
                MGTools::make_sparsity_pattern(dof_handler, dsp, level);
                dsp.compress();
                SparsityTools::distribute_sparsity_pattern(
                  dsp,
                  dof_handler.locally_owned_mg_dofs(level),
                  mpi_communicator,
                  dof_set);

                mg_matrix[level].reinit(
                  dof_handler.locally_owned_mg_dofs(level),
                  dof_handler.locally_owned_mg_dofs(level),
                  dsp,
                  mpi_communicator);
#else
                TrilinosWrappers::SparsityPattern dsp(
                  dof_handler.locally_owned_mg_dofs(level),
                  dof_handler.locally_owned_mg_dofs(level),
                  dof_set,
                  mpi_communicator);
                MGTools::make_sparsity_pattern(dof_handler, dsp, level);

                dsp.compress();
                mg_matrix[level].reinit(dsp);
#endif
              }

              {
#ifdef USE_PETSC_LA
                DynamicSparsityPattern dsp(dof_set);
                MGTools::make_interface_sparsity_pattern(dof_handler,
                                                         mg_constrained_dofs,
                                                         dsp,
                                                         level);
                dsp.compress();
                SparsityTools::distribute_sparsity_pattern(
                  dsp,
                  dof_handler.locally_owned_mg_dofs(level),
                  mpi_communicator,
                  dof_set);

                mg_interface_in[level].reinit(
                  dof_handler.locally_owned_mg_dofs(level),
                  dof_handler.locally_owned_mg_dofs(level),
                  dsp,
                  mpi_communicator);
#else
                TrilinosWrappers::SparsityPattern dsp(
                  dof_handler.locally_owned_mg_dofs(level),
                  dof_handler.locally_owned_mg_dofs(level),
                  dof_set,
                  mpi_communicator);

                MGTools::make_interface_sparsity_pattern(dof_handler,
                                                         mg_constrained_dofs,
                                                         dsp,
                                                         level);
                dsp.compress();
                mg_interface_in[level].reinit(dsp);
#endif
              }
            }
          break;
        }

      default:
        Assert(false, ExcNotImplemented());
    }
}


// @sect4{LaplaceProblem::assemble_system()}

// The assembly is split into three parts: `assemble_system()`,
// `assemble_multigrid()`, and `assemble_rhs()`. The
// `assemble_system()` function here assembles and stores the (global)
// system matrix and the right-hand side for the matrix-based
// methods. It is similar to the assembly in step-40.
//
// Note that the matrix-free method does not execute this function as it does
// not need to assemble a matrix, and it will instead assemble the right-hand
// side in assemble_rhs().
template <int dim, int degree>
void LaplaceProblem<dim, degree>::assemble_system()
{
  TimerOutput::Scope timing(computing_timer, "Assemble");

  const QGauss<dim> quadrature_formula(degree + 1);

  FEValues<dim> fe_values(fe,
                          quadrature_formula,
                          update_values | update_gradients |
                            update_quadrature_points | update_JxW_values);

  const unsigned int dofs_per_cell = fe.n_dofs_per_cell();
  const unsigned int n_q_points    = quadrature_formula.size();

  FullMatrix<double> cell_matrix(dofs_per_cell, dofs_per_cell);
  Vector<double>     cell_rhs(dofs_per_cell);

  std::vector<types::global_dof_index> local_dof_indices(dofs_per_cell);

  const Coefficient<dim> coefficient;
  RightHandSide<dim>     rhs;
  std::vector<double>    rhs_values(n_q_points);

  for (const auto &cell : dof_handler.active_cell_iterators())
    if (cell->is_locally_owned())
      {
        cell_matrix = 0;
        cell_rhs    = 0;

        fe_values.reinit(cell);

        const double coefficient_value =
          coefficient.average_value(fe_values.get_quadrature_points());
        rhs.value_list(fe_values.get_quadrature_points(), rhs_values);

        for (unsigned int q_point = 0; q_point < n_q_points; ++q_point)
          for (unsigned int i = 0; i < dofs_per_cell; ++i)
            {
              for (unsigned int j = 0; j < dofs_per_cell; ++j)
                cell_matrix(i, j) +=
                  coefficient_value *                // epsilon(x)
                  fe_values.shape_grad(i, q_point) * // * grad phi_i(x)
                  fe_values.shape_grad(j, q_point) * // * grad phi_j(x)
                  fe_values.JxW(q_point);            // * dx

              cell_rhs(i) +=
                fe_values.shape_value(i, q_point) * // grad phi_i(x)
                rhs_values[q_point] *               // * f(x)
                fe_values.JxW(q_point);             // * dx
            }

        cell->get_dof_indices(local_dof_indices);
        constraints.distribute_local_to_global(cell_matrix,
                                               cell_rhs,
                                               local_dof_indices,
                                               system_matrix,
                                               right_hand_side);
      }

  system_matrix.compress(VectorOperation::add);
  right_hand_side.compress(VectorOperation::add);
}


// @sect4{LaplaceProblem::assemble_multigrid()}

// The following function assembles and stores the multilevel matrices for the
// matrix-based GMG method. This function is similar to the one found in
// step-16, only here it works for distributed meshes. This difference amounts
// to adding a condition that we only assemble on locally owned level cells and
// a call to compress() for each matrix that is built.
template <int dim, int degree>
void LaplaceProblem<dim, degree>::assemble_multigrid()
{
  TimerOutput::Scope timing(computing_timer, "Assemble multigrid");

  QGauss<dim> quadrature_formula(degree + 1);

  FEValues<dim> fe_values(fe,
                          quadrature_formula,
                          update_values | update_gradients |
                            update_quadrature_points | update_JxW_values);

  const unsigned int dofs_per_cell = fe.n_dofs_per_cell();
  const unsigned int n_q_points    = quadrature_formula.size();

  FullMatrix<double> cell_matrix(dofs_per_cell, dofs_per_cell);

  std::vector<types::global_dof_index> local_dof_indices(dofs_per_cell);

  const Coefficient<dim> coefficient;

  std::vector<AffineConstraints<double>> boundary_constraints(
    triangulation.n_global_levels());
  for (unsigned int level = 0; level < triangulation.n_global_levels(); ++level)
    {
      const IndexSet dof_set =
        DoFTools::extract_locally_relevant_level_dofs(dof_handler, level);
      boundary_constraints[level].reinit(dof_set);
      boundary_constraints[level].add_lines(
        mg_constrained_dofs.get_refinement_edge_indices(level));
      boundary_constraints[level].add_lines(
        mg_constrained_dofs.get_boundary_indices(level));

      boundary_constraints[level].close();
    }

  for (const auto &cell : dof_handler.cell_iterators())
    if (cell->level_subdomain_id() == triangulation.locally_owned_subdomain())
      {
        cell_matrix = 0;
        fe_values.reinit(cell);

        const double coefficient_value =
          coefficient.average_value(fe_values.get_quadrature_points());

        for (unsigned int q_point = 0; q_point < n_q_points; ++q_point)
          for (unsigned int i = 0; i < dofs_per_cell; ++i)
            for (unsigned int j = 0; j < dofs_per_cell; ++j)
              cell_matrix(i, j) +=
                coefficient_value * fe_values.shape_grad(i, q_point) *
                fe_values.shape_grad(j, q_point) * fe_values.JxW(q_point);

        cell->get_mg_dof_indices(local_dof_indices);

        boundary_constraints[cell->level()].distribute_local_to_global(
          cell_matrix, local_dof_indices, mg_matrix[cell->level()]);

        for (unsigned int i = 0; i < dofs_per_cell; ++i)
          for (unsigned int j = 0; j < dofs_per_cell; ++j)
            if (mg_constrained_dofs.is_interface_matrix_entry(
                  cell->level(), local_dof_indices[i], local_dof_indices[j]))
              mg_interface_in[cell->level()].add(local_dof_indices[i],
                                                 local_dof_indices[j],
                                                 cell_matrix(i, j));
      }

  for (unsigned int i = 0; i < triangulation.n_global_levels(); ++i)
    {
      mg_matrix[i].compress(VectorOperation::add);
      mg_interface_in[i].compress(VectorOperation::add);
    }
}



// @sect4{LaplaceProblem::assemble_rhs()}

// The final function in this triptych assembles the right-hand side
// vector for the matrix-free method -- because in the matrix-free
// framework, we don't have to assemble the matrix and can get away
// with only assembling the right hand side. We could do this by extracting the
// code from the `assemble_system()` function above that deals with the right
// hand side, but we decide instead to go all in on the matrix-free approach and
// do the assembly using that way as well.
//
// The result is a function that is similar
// to the one found in the "Use FEEvaluation::read_dof_values_plain()
// to avoid resolving constraints" subsection in the "Possibilities
// for extensions" section of step-37.
//
// The reason for this function is that the MatrixFree operators do not take
// into account non-homogeneous Dirichlet constraints, instead treating all
// Dirichlet constraints as homogeneous. To account for this, the right-hand
// side here is assembled as the residual $r_0 = f-Au_0$, where $u_0$ is a
// zero vector except in the Dirichlet values. Then when solving, we have that
// the solution is $u = u_0 + A^{-1}r_0$. This can be seen as a Newton
// iteration on a linear system with initial guess $u_0$. The CG solve in the
// `solve()` function below computes $A^{-1}r_0$ and the call to
// `constraints.distribute()` (which directly follows) adds the $u_0$.
//
// Obviously, since we are considering a problem with zero Dirichlet boundary,
// we could have taken a similar approach to step-37 `assemble_rhs()`, but this
// additional work allows us to change the problem declaration if we so
// choose.
//
// This function has two parts in the integration loop: applying the negative
// of matrix $A$ to $u_0$ by submitting the negative of the gradient, and adding
// the right-hand side contribution by submitting the value $f$. We must be sure
// to use `read_dof_values_plain()` for evaluating $u_0$ as `read_dof_values()`
// would set all Dirichlet values to zero.
//
// Finally, the system_rhs vector is of type LA::MPI::Vector, but the
// MatrixFree class only work for
// LinearAlgebra::distributed::Vector.  Therefore we must
// compute the right-hand side using MatrixFree functionality and then
// use the functions in the `ChangeVectorType` namespace to copy it to
// the correct type.
template <int dim, int degree>
void LaplaceProblem<dim, degree>::assemble_rhs()
{
  TimerOutput::Scope timing(computing_timer, "Assemble right-hand side");

  MatrixFreeActiveVector solution_copy;
  MatrixFreeActiveVector right_hand_side_copy;
  mf_system_matrix.initialize_dof_vector(solution_copy);
  mf_system_matrix.initialize_dof_vector(right_hand_side_copy);

  solution_copy = 0.;
  constraints.distribute(solution_copy);
  solution_copy.update_ghost_values();
  right_hand_side_copy = 0;
  const Table<2, VectorizedArray<double>> &coefficient =
    *(mf_system_matrix.get_coefficient());

  RightHandSide<dim> right_hand_side_function;

  FEEvaluation<dim, degree, degree + 1, 1, double> phi(
    *mf_system_matrix.get_matrix_free());

  for (unsigned int cell = 0;
       cell < mf_system_matrix.get_matrix_free()->n_cell_batches();
       ++cell)
    {
      phi.reinit(cell);
      phi.read_dof_values_plain(solution_copy);
      phi.evaluate(EvaluationFlags::gradients);

      for (unsigned int q = 0; q < phi.n_q_points; ++q)
        {
          phi.submit_gradient(-1.0 *
                                (coefficient(cell, 0) * phi.get_gradient(q)),
                              q);
          phi.submit_value(
            right_hand_side_function.value(phi.quadrature_point(q)), q);
        }

      phi.integrate_scatter(EvaluationFlags::values |
                              EvaluationFlags::gradients,
                            right_hand_side_copy);
    }

  right_hand_side_copy.compress(VectorOperation::add);

  ChangeVectorTypes::copy(right_hand_side, right_hand_side_copy);
}



// @sect4{LaplaceProblem::solve()}

// Here we set up the multigrid preconditioner, test the timing of a single
// V-cycle, and solve the linear system. Unsurprisingly, this is one of the
// places where the three methods differ the most.
template <int dim, int degree>
void LaplaceProblem<dim, degree>::solve()
{
  TimerOutput::Scope timing(computing_timer, "Solve");

  SolverControl solver_control(1000, 1.e-10 * right_hand_side.l2_norm());
  solver_control.enable_history_data();

  solution = 0.;

  // The solver for the matrix-free GMG method is similar to step-37, apart
  // from adding some interface matrices in complete analogy to step-16.
  switch (settings.solver)
    {
      case Settings::gmg_mf:
        {
          computing_timer.enter_subsection("Solve: Preconditioner setup");

          MGTransferMatrixFree<dim, float> mg_transfer(mg_constrained_dofs);
          mg_transfer.build(dof_handler);

          SolverControl coarse_solver_control(1000, 1e-12, false, false);
          SolverCG<MatrixFreeLevelVector> coarse_solver(coarse_solver_control);
          PreconditionIdentity            identity;
          MGCoarseGridIterativeSolver<MatrixFreeLevelVector,
                                      SolverCG<MatrixFreeLevelVector>,
                                      MatrixFreeLevelMatrix,
                                      PreconditionIdentity>
            coarse_grid_solver(coarse_solver, mf_mg_matrix[0], identity);

          using Smoother = PreconditionJacobi<MatrixFreeLevelMatrix>;
          MGSmootherPrecondition<MatrixFreeLevelMatrix,
                                 Smoother,
                                 MatrixFreeLevelVector>
            smoother;
          smoother.initialize(mf_mg_matrix,
                              typename Smoother::AdditionalData(
                                settings.smoother_dampen));
          smoother.set_steps(settings.smoother_steps);

          mg::Matrix<MatrixFreeLevelVector> mg_m(mf_mg_matrix);

          MGLevelObject<
            MatrixFreeOperators::MGInterfaceOperator<MatrixFreeLevelMatrix>>
            mg_interface_matrices;
          mg_interface_matrices.resize(0, triangulation.n_global_levels() - 1);
          for (unsigned int level = 0; level < triangulation.n_global_levels();
               ++level)
            mg_interface_matrices[level].initialize(mf_mg_matrix[level]);
          mg::Matrix<MatrixFreeLevelVector> mg_interface(mg_interface_matrices);

          Multigrid<MatrixFreeLevelVector> mg(
            mg_m, coarse_grid_solver, mg_transfer, smoother, smoother);
          mg.set_edge_matrices(mg_interface, mg_interface);

          PreconditionMG<dim,
                         MatrixFreeLevelVector,
                         MGTransferMatrixFree<dim, float>>
            preconditioner(dof_handler, mg, mg_transfer);

          // Copy the solution vector and right-hand side from LA::MPI::Vector
          // to LinearAlgebra::distributed::Vector so that we can solve.
          MatrixFreeActiveVector solution_copy;
          MatrixFreeActiveVector right_hand_side_copy;
          mf_system_matrix.initialize_dof_vector(solution_copy);
          mf_system_matrix.initialize_dof_vector(right_hand_side_copy);

          ChangeVectorTypes::copy(solution_copy, solution);
          ChangeVectorTypes::copy(right_hand_side_copy, right_hand_side);
          computing_timer.leave_subsection("Solve: Preconditioner setup");

          // Timing for 1 V-cycle.
          {
            TimerOutput::Scope timing(computing_timer,
                                      "Solve: 1 multigrid V-cycle");
            preconditioner.vmult(solution_copy, right_hand_side_copy);
          }
          solution_copy = 0.;

          // Solve the linear system, update the ghost values of the solution,
          // copy back to LA::MPI::Vector and distribute constraints.
          {
            SolverCG<MatrixFreeActiveVector> solver(solver_control);

            TimerOutput::Scope timing(computing_timer, "Solve: CG");
            solver.solve(mf_system_matrix,
                         solution_copy,
                         right_hand_side_copy,
                         preconditioner);
          }

          solution_copy.update_ghost_values();
          ChangeVectorTypes::copy(solution, solution_copy);
          constraints.distribute(solution);

          break;
        }

        // Solver for the matrix-based GMG method, similar to step-16, only
        // using a Jacobi smoother instead of a SOR smoother (which is not
        // implemented in parallel).
      case Settings::gmg_mb:
        {
          computing_timer.enter_subsection("Solve: Preconditioner setup");

          MGTransferPrebuilt<VectorType> mg_transfer(mg_constrained_dofs);
          mg_transfer.build(dof_handler);

          SolverControl        coarse_solver_control(1000, 1e-12, false, false);
          SolverCG<VectorType> coarse_solver(coarse_solver_control);
          PreconditionIdentity identity;
          MGCoarseGridIterativeSolver<VectorType,
                                      SolverCG<VectorType>,
                                      MatrixType,
                                      PreconditionIdentity>
            coarse_grid_solver(coarse_solver, mg_matrix[0], identity);

          using Smoother = LA::MPI::PreconditionJacobi;
          MGSmootherPrecondition<MatrixType, Smoother, VectorType> smoother;

#ifdef USE_PETSC_LA
          smoother.initialize(mg_matrix);
          Assert(
            settings.smoother_dampen == 1.0,
            ExcNotImplemented(
              "PETSc's PreconditionJacobi has no support for a damping parameter."));
#else
          smoother.initialize(mg_matrix, settings.smoother_dampen);
#endif

          smoother.set_steps(settings.smoother_steps);

          mg::Matrix<VectorType> mg_m(mg_matrix);
          mg::Matrix<VectorType> mg_in(mg_interface_in);
          mg::Matrix<VectorType> mg_out(mg_interface_in);

          Multigrid<VectorType> mg(
            mg_m, coarse_grid_solver, mg_transfer, smoother, smoother);
          mg.set_edge_matrices(mg_out, mg_in);


          PreconditionMG<dim, VectorType, MGTransferPrebuilt<VectorType>>
            preconditioner(dof_handler, mg, mg_transfer);

          computing_timer.leave_subsection("Solve: Preconditioner setup");

          // Timing for 1 V-cycle.
          {
            TimerOutput::Scope timing(computing_timer,
                                      "Solve: 1 multigrid V-cycle");
            preconditioner.vmult(solution, right_hand_side);
          }
          solution = 0.;

          // Solve the linear system and distribute constraints.
          {
            SolverCG<VectorType> solver(solver_control);

            TimerOutput::Scope timing(computing_timer, "Solve: CG");
            solver.solve(system_matrix,
                         solution,
                         right_hand_side,
                         preconditioner);
          }

          constraints.distribute(solution);

          break;
        }

      // Solver for the AMG method, similar to step-40.
      case Settings::amg:
        {
          computing_timer.enter_subsection("Solve: Preconditioner setup");

          PreconditionAMG                 preconditioner;
          PreconditionAMG::AdditionalData Amg_data;

#ifdef USE_PETSC_LA
          Amg_data.symmetric_operator = true;
#else
          Amg_data.elliptic              = true;
          Amg_data.smoother_type         = "Jacobi";
          Amg_data.higher_order_elements = true;
          Amg_data.smoother_sweeps       = settings.smoother_steps;
          Amg_data.aggregation_threshold = 0.02;
#endif

          Amg_data.output_details = false;

          preconditioner.initialize(system_matrix, Amg_data);
          computing_timer.leave_subsection("Solve: Preconditioner setup");

          // Timing for 1 V-cycle.
          {
            TimerOutput::Scope timing(computing_timer,
                                      "Solve: 1 multigrid V-cycle");
            preconditioner.vmult(solution, right_hand_side);
          }
          solution = 0.;

          // Solve the linear system and distribute constraints.
          {
            SolverCG<VectorType> solver(solver_control);

            TimerOutput::Scope timing(computing_timer, "Solve: CG");
            solver.solve(system_matrix,
                         solution,
                         right_hand_side,
                         preconditioner);
          }
          constraints.distribute(solution);

          break;
        }

      default:
        Assert(false, ExcInternalError());
    }

  pcout << "   Number of CG iterations:      " << solver_control.last_step()
        << std::endl;
}


// @sect3{The error estimator}

// We use the FEInterfaceValues class to assemble an error estimator to decide
// which cells to refine. See the exact definition of the cell and face
// integrals in the introduction. To use the method, we define Scratch and
// Copy objects for the MeshWorker::mesh_loop() with much of the following code
// being in essence as was set up in step-12 already (or at least similar in
// spirit).
template <int dim>
struct ScratchData
{
  ScratchData(const Mapping<dim> &      mapping,
              const FiniteElement<dim> &fe,
              const unsigned int        quadrature_degree,
              const UpdateFlags         update_flags,
              const UpdateFlags         interface_update_flags)
    : fe_values(mapping, fe, QGauss<dim>(quadrature_degree), update_flags)
    , fe_interface_values(mapping,
                          fe,
                          QGauss<dim - 1>(quadrature_degree),
                          interface_update_flags)
  {}


  ScratchData(const ScratchData<dim> &scratch_data)
    : fe_values(scratch_data.fe_values.get_mapping(),
                scratch_data.fe_values.get_fe(),
                scratch_data.fe_values.get_quadrature(),
                scratch_data.fe_values.get_update_flags())
    , fe_interface_values(scratch_data.fe_values.get_mapping(),
                          scratch_data.fe_values.get_fe(),
                          scratch_data.fe_interface_values.get_quadrature(),
                          scratch_data.fe_interface_values.get_update_flags())
  {}

  FEValues<dim>          fe_values;
  FEInterfaceValues<dim> fe_interface_values;
};



struct CopyData
{
  CopyData()
    : cell_index(numbers::invalid_unsigned_int)
    , value(0.)
  {}

  struct FaceData
  {
    unsigned int cell_indices[2];
    double       values[2];
  };

  unsigned int          cell_index;
  double                value;
  std::vector<FaceData> face_data;
};


template <int dim, int degree>
void LaplaceProblem<dim, degree>::estimate()
{
  TimerOutput::Scope timing(computing_timer, "Estimate");

  VectorType temp_solution;
  temp_solution.reinit(locally_owned_dofs,
                       locally_relevant_dofs,
                       mpi_communicator);
  temp_solution = solution;

  const Coefficient<dim> coefficient;

  estimated_error_square_per_cell.reinit(triangulation.n_active_cells());

  using Iterator = typename DoFHandler<dim>::active_cell_iterator;

  // Assembler for cell residual $h^2 \| f + \epsilon \triangle u \|_K^2$
  auto cell_worker = [&](const Iterator &  cell,
                         ScratchData<dim> &scratch_data,
                         CopyData &        copy_data) {
    FEValues<dim> &fe_values = scratch_data.fe_values;
    fe_values.reinit(cell);

    RightHandSide<dim> rhs;
    const double       rhs_value = rhs.value(cell->center());

    const double nu = coefficient.value(cell->center());

    std::vector<Tensor<2, dim>> hessians(fe_values.n_quadrature_points);
    fe_values.get_function_hessians(temp_solution, hessians);

    copy_data.cell_index = cell->active_cell_index();

    double residual_norm_square = 0.;
    for (unsigned k = 0; k < fe_values.n_quadrature_points; ++k)
      {
        const double residual = (rhs_value + nu * trace(hessians[k]));
        residual_norm_square += residual * residual * fe_values.JxW(k);
      }

    copy_data.value =
      cell->diameter() * cell->diameter() * residual_norm_square;
  };

  // Assembler for face term $\sum_F h_F \| \jump{\epsilon \nabla u \cdot n}
  // \|_F^2$
  auto face_worker = [&](const Iterator &    cell,
                         const unsigned int &f,
                         const unsigned int &sf,
                         const Iterator &    ncell,
                         const unsigned int &nf,
                         const unsigned int &nsf,
                         ScratchData<dim> &  scratch_data,
                         CopyData &          copy_data) {
    FEInterfaceValues<dim> &fe_interface_values =
      scratch_data.fe_interface_values;
    fe_interface_values.reinit(cell, f, sf, ncell, nf, nsf);

    copy_data.face_data.emplace_back();
    CopyData::FaceData &copy_data_face = copy_data.face_data.back();

    copy_data_face.cell_indices[0] = cell->active_cell_index();
    copy_data_face.cell_indices[1] = ncell->active_cell_index();

    const double coeff1 = coefficient.value(cell->center());
    const double coeff2 = coefficient.value(ncell->center());

    std::vector<Tensor<1, dim>> grad_u[2];

    for (unsigned int i = 0; i < 2; ++i)
      {
        grad_u[i].resize(fe_interface_values.n_quadrature_points);
        fe_interface_values.get_fe_face_values(i).get_function_gradients(
          temp_solution, grad_u[i]);
      }

    double jump_norm_square = 0.;

    for (unsigned int qpoint = 0;
         qpoint < fe_interface_values.n_quadrature_points;
         ++qpoint)
      {
        const double jump =
          coeff1 * grad_u[0][qpoint] * fe_interface_values.normal(qpoint) -
          coeff2 * grad_u[1][qpoint] * fe_interface_values.normal(qpoint);

        jump_norm_square += jump * jump * fe_interface_values.JxW(qpoint);
      }

    const double h           = cell->face(f)->measure();
    copy_data_face.values[0] = 0.5 * h * jump_norm_square;
    copy_data_face.values[1] = copy_data_face.values[0];
  };

  auto copier = [&](const CopyData &copy_data) {
    if (copy_data.cell_index != numbers::invalid_unsigned_int)
      estimated_error_square_per_cell[copy_data.cell_index] += copy_data.value;

    for (auto &cdf : copy_data.face_data)
      for (unsigned int j = 0; j < 2; ++j)
        estimated_error_square_per_cell[cdf.cell_indices[j]] += cdf.values[j];
  };

  const unsigned int n_gauss_points = degree + 1;
  ScratchData<dim>   scratch_data(mapping,
                                fe,
                                n_gauss_points,
                                update_hessians | update_quadrature_points |
                                  update_JxW_values,
                                update_values | update_gradients |
                                  update_JxW_values | update_normal_vectors);
  CopyData           copy_data;

  // We need to assemble each interior face once but we need to make sure that
  // both processes assemble the face term between a locally owned and a ghost
  // cell. This is achieved by setting the
  // MeshWorker::assemble_ghost_faces_both flag. We need to do this, because
  // we do not communicate the error estimator contributions here.
  MeshWorker::mesh_loop(dof_handler.begin_active(),
                        dof_handler.end(),
                        cell_worker,
                        copier,
                        scratch_data,
                        copy_data,
                        MeshWorker::assemble_own_cells |
                          MeshWorker::assemble_ghost_faces_both |
                          MeshWorker::assemble_own_interior_faces_once,
                        /*boundary_worker=*/nullptr,
                        face_worker);

  const double global_error_estimate =
    std::sqrt(Utilities::MPI::sum(estimated_error_square_per_cell.l1_norm(),
                                  mpi_communicator));
  pcout << "   Global error estimate:        " << global_error_estimate
        << std::endl;
}


// @sect4{LaplaceProblem::refine_grid()}

// We use the cell-wise estimator stored in the vector @p estimate_vector and
// refine a fixed number of cells (chosen here to roughly double the number of
// DoFs in each step).
template <int dim, int degree>
void LaplaceProblem<dim, degree>::refine_grid()
{
  TimerOutput::Scope timing(computing_timer, "Refine grid");

  const double refinement_fraction = 1. / (std::pow(2.0, dim) - 1.);
  parallel::distributed::GridRefinement::refine_and_coarsen_fixed_number(
    triangulation, estimated_error_square_per_cell, refinement_fraction, 0.0);

  triangulation.execute_coarsening_and_refinement();
}


// @sect4{LaplaceProblem::output_results()}

// The output_results() function is similar to the ones found in many of the
// tutorials (see step-40 for example).
template <int dim, int degree>
void LaplaceProblem<dim, degree>::output_results(const unsigned int cycle)
{
  TimerOutput::Scope timing(computing_timer, "Output results");

  VectorType temp_solution;
  temp_solution.reinit(locally_owned_dofs,
                       locally_relevant_dofs,
                       mpi_communicator);
  temp_solution = solution;

  DataOut<dim> data_out;
  data_out.attach_dof_handler(dof_handler);
  data_out.add_data_vector(temp_solution, "solution");

  Vector<float> subdomain(triangulation.n_active_cells());
  for (unsigned int i = 0; i < subdomain.size(); ++i)
    subdomain(i) = triangulation.locally_owned_subdomain();
  data_out.add_data_vector(subdomain, "subdomain");

  Vector<float> level(triangulation.n_active_cells());
  for (const auto &cell : triangulation.active_cell_iterators())
    level(cell->active_cell_index()) = cell->level();
  data_out.add_data_vector(level, "level");

  if (estimated_error_square_per_cell.size() > 0)
    data_out.add_data_vector(estimated_error_square_per_cell,
                             "estimated_error_square_per_cell");

  data_out.build_patches();

  const std::string pvtu_filename = data_out.write_vtu_with_pvtu_record(
    "", "solution", cycle, mpi_communicator, 2 /*n_digits*/, 1 /*n_groups*/);

  pcout << "   Wrote " << pvtu_filename << std::endl;
}


// @sect4{LaplaceProblem::run()}

// As in most tutorials, this function calls the various functions defined
// above to set up, assemble, solve, and output the results.
template <int dim, int degree>
void LaplaceProblem<dim, degree>::run()
{
  for (unsigned int cycle = 0; cycle < settings.n_steps; ++cycle)
    {
      pcout << "Cycle " << cycle << ':' << std::endl;
      if (cycle > 0)
        refine_grid();

      pcout << "   Number of active cells:       "
            << triangulation.n_global_active_cells();

      // We only output level cell data for the GMG methods (same with DoF
      // data below). Note that the partition efficiency is irrelevant for AMG
      // since the level hierarchy is not distributed or used during the
      // computation.
      if (settings.solver == Settings::gmg_mf ||
          settings.solver == Settings::gmg_mb)
        pcout << " (" << triangulation.n_global_levels() << " global levels)"
              << std::endl
              << "   Partition efficiency:         "
              << 1.0 / MGTools::workload_imbalance(triangulation);
      pcout << std::endl;

      setup_system();

      // Only set up the multilevel hierarchy for GMG.
      if (settings.solver == Settings::gmg_mf ||
          settings.solver == Settings::gmg_mb)
        setup_multigrid();

      pcout << "   Number of degrees of freedom: " << dof_handler.n_dofs();
      if (settings.solver == Settings::gmg_mf ||
          settings.solver == Settings::gmg_mb)
        {
          pcout << " (by level: ";
          for (unsigned int level = 0; level < triangulation.n_global_levels();
               ++level)
            pcout << dof_handler.n_dofs(level)
                  << (level == triangulation.n_global_levels() - 1 ? ")" :
                                                                     ", ");
        }
      pcout << std::endl;

      // For the matrix-free method, we only assemble the right-hand side.
      // For both matrix-based methods, we assemble both active matrix and
      // right-hand side, and only assemble the multigrid matrices for
      // matrix-based GMG.
      if (settings.solver == Settings::gmg_mf)
        assemble_rhs();
      else /*gmg_mb or amg*/
        {
          assemble_system();
          if (settings.solver == Settings::gmg_mb)
            assemble_multigrid();
        }

      solve();
      estimate();

      if (settings.output)
        output_results(cycle);

      computing_timer.print_summary();
      computing_timer.reset();
    }
}


// @sect3{The main() function}

// This is a similar main function to step-40, with the exception that
// we require the user to pass a .prm file as a sole command line
// argument (see step-29 and the documentation of the ParameterHandler
// class for a complete discussion of parameter files).
int main(int argc, char *argv[])
{
  using namespace dealii;
  Utilities::MPI::MPI_InitFinalize mpi_initialization(argc, argv, 1);

  Settings settings;
  if (!settings.try_parse((argc > 1) ? (argv[1]) : ""))
    return 0;

  try
    {
      constexpr unsigned int fe_degree = 2;

      switch (settings.dimension)
        {
          case 2:
            {
              LaplaceProblem<2, fe_degree> test(settings);
              test.run();

              break;
            }

          case 3:
            {
              LaplaceProblem<3, fe_degree> test(settings);
              test.run();

              break;
            }

          default:
            Assert(false, ExcMessage("This program only works in 2d and 3d."));
        }
    }
  catch (std::exception &exc)
    {
      std::cerr << std::endl
                << std::endl
                << "----------------------------------------------------"
                << std::endl;
      std::cerr << "Exception on processing: " << std::endl
                << exc.what() << std::endl
                << "Aborting!" << std::endl
                << "----------------------------------------------------"
                << std::endl;
      MPI_Abort(MPI_COMM_WORLD, 1);
      return 1;
    }
  catch (...)
    {
      std::cerr << std::endl
                << std::endl
                << "----------------------------------------------------"
                << std::endl;
      std::cerr << "Unknown exception!" << std::endl
                << "Aborting!" << std::endl
                << "----------------------------------------------------"
                << std::endl;
      MPI_Abort(MPI_COMM_WORLD, 2);
      return 1;
    }

  return 0;
}
